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Computer Vision for Biomedical Image Applications

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 9835

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Special Issue Editors

Prof. Stavros A. Karkanis

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Guest Editor

University of Thessaly, Volos, Greece
Interests: Image Processing; Computer Vision; Artificial Intelligence; Deep Learning; Biomedical Applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Evaggelos Spyrou

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Guest Editor

Department of Informatics and Telecommunications, University of Thessaly, Thessaly, Greece
Interests: multimedia analysis; computer vision; human activity recognition; emotion recognition; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Michalis Savelonas

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Guest Editor

Department of Computer Science and Biomedical Informatics, School of Science, Campus of Lamia, University of Thessaly, GR-35131 Lamia, Greece
Interests: pattern recognition; computer vision; expert systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The MDPI Information Journal invites submissions to a special issue on “Computer Vision for Biomedical Image Applications”

Computer Vision (CV) has become instrumental in biomedical applications, supporting physicians in various tasks, such as diagnostic decisions, surgeries, medical image/video analysis and medical research. CV processes aim to simulate an expert’s eye in locating normal areas or suspicious patterns on medical imagery, providing a second opinion in critical diagnoses. Moreover, the raw medical imaging data ask for interpretation, in order to extract information which is valuable for the expert, as well as to create an enriched decision framework for physicians to understand each health case and predict its progress. CV can save a significant amount of human effort and aid in saving lives.

Machine Learning (ML) offers algorithms capable of learning complex and high-dimensional patterns. Equipped with feedback mechanisms such as backpropagation, machine learning algorithms are capable to acquire expertise and propose diagnoses on similar cases with a considerable success rate.

Another ML-related aspect is the increasing number of connected medical devices, which results in amounts of data reaching the scale of up to zetabytes (ZB) per year. In this light, The Computer Vision for Biomedical Image Applications issue aims to address the recent paradigm of Deep Learning, which naturally copes with Big Data.

The combined use of CV and ML in biomedical applications triggered a new era in computerized medical diagnosis. Today, a huge number of efficient approaches / applications and systems are final products and support medical decisions in fields like dermatology, radiology, cardiology, embryology etc.

This Special Issue is concerned with ground-breaking topics of Computer Vision for Biomedical Applications.

Prof. Stavros A. Karkanis
Prof. Evaggelos Spyrou
Prof. Michalis Savelonas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Medical Image Analysis
Medical Computer Vision
Computer Vision for Medical Diagnosis
IoMT (Internet of Medical Things)
Cloud Computing and Big Data for Healthcare
Artificial Intelligence on Medical Applications
Machine Learning in Medicine
Augmented Reality for Medical Diagnosis
Medical Imaging
Image Processing
Computer-Aided Diagnosis
Image-Guided Procedures
Robotic Interventions
Image Perception
Observer Performance
Digital Pathology
Biomedical Applications in Molecular, Structural and Functional Imaging
Computational Physiology
Ultrasonic Imaging and Tomography

Published Papers (2 papers)

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Research

22 pages, 5662 KiB

Open AccessArticle

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

by Fatima Sajid Butt, Luigi La Blunda, Matthias F. Wagner, Jörg Schäfer, Inmaculada Medina-Bulo and David Gómez-Ullate

Information 2021, 12(2), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/info12020063 - 03 Feb 2021

Cited by 16 | Viewed by 4414

Abstract

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems. Full article

(This article belongs to the Special Issue Computer Vision for Biomedical Image Applications)

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15 pages, 4368 KiB

Open AccessArticle

Bimodal CT/MRI-Based Segmentation Method for Intervertebral Disc Boundary Extraction

by Meletios Liaskos, Michalis A. Savelonas, Pantelis A. Asvestas, Marios G. Lykissas and George K. Matsopoulos

Information 2020, 11(9), 448; https://0-doi-org.brum.beds.ac.uk/10.3390/info11090448 - 15 Sep 2020

Cited by 6 | Viewed by 3588

Abstract

Intervertebral disc (IVD) localization and segmentation have triggered intensive research efforts in the medical image analysis community, since IVD abnormalities are strong indicators of various spinal cord-related pathologies. Despite the intensive research efforts to address IVD boundary extraction based on MR images, the potential of bimodal approaches, which benefit from complementary information derived from both magnetic resonance imaging (MRI) and computed tomography (CT), has not yet been fully realized. Furthermore, most existing approaches rely on manual intervention or on learning, although sufficiently large and labelled 3D datasets are not always available. In this light, this work introduces a bimodal segmentation method for vertebrae and IVD boundary extraction, which requires a limited amount of intervention and is not based on learning. The proposed method comprises various image processing and analysis stages, including CT/MRI registration, Otsu-based thresholding and Chan–Vese-based segmentation. The method was applied on 98 expert-annotated pairs of CT and MR spinal cord images with varying slice thicknesses and pixel sizes, which were obtained from 7 patients using different scanners. The experimental results had a Dice similarity coefficient equal to 94.77(%) for CT and 86.26(%) for MRI and a Hausdorff distance equal to 4.4 pixels for CT and 4.5 pixels for MRI. Experimental comparisons with state-of-the-art CT and MRI segmentation methods lead to the conclusion that the proposed method provides a reliable alternative for vertebrae and IVD boundary extraction. Moreover, the segmentation results are utilized to perform a bimodal visualization of the spine, which could potentially aid differential diagnosis with respect to several spine-related pathologies. Full article

(This article belongs to the Special Issue Computer Vision for Biomedical Image Applications)

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